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The discovery of SnSe single crystals with record high thermoelectric efficiency along the b -axis has led to the search for ways to synthesize polycrystalline SnSe with similar efficiencies. However, due to weak texturing and difficulties in doping, such high thermoelectric efficiencies have not been realized in polycrystals or thin films. Here, we show that highly textured and hole doped SnSe thin films with thermoelectric power factors at the single crystal level can be prepared by solution process. Purification step in the synthetic process produced a SnSe-based chalcogenidometallate precursor, which decomposes to form the SnSe 2 phase. We show that the strong textures of the thin films in the b–c plane originate from the transition of two dimensional SnSe 2 to SnSe. This composition change-driven transition offers wide control over composition and doping of the thin films. Our optimum SnSe thin films exhibit a thermoelectric power factor of 4.27 μW cm −1 K −2 . Despite significant efforts to improve the thermoelectric properties of polycrystalline SnSe, precise control of texturing and doping is a challenge. Here, the authors report hole doped and highly textured SnSe thin films prepared by low cost, scalable solution processing.

With the increasing digitalization of maritime transportation, the demand for structured and interoperable data has grown. While the S-100 framework developed by the International Hydrographic Organization (IHO) provides a foundation for standardizing maritime information, a data model for representing marine casualties has not yet been developed. As a result, past incident data—such as collisions or groundings—remain fragmented in unstructured formats and are excluded from electronic navigational systems, limiting their use in safety analysis and route planning. To address this gap, this paper proposes a data model for structuring and visualizing marine casualty information within the S-100 standard. The model was designed by defining an application schema, constructing a machine-readable feature catalogue, and developing a portrayal catalogue and custom symbology for integration into Electronic Navigational Charts (ENCs). A case study using actual casualty records was conducted to examine whether the model satisfies the structural and portrayal requirements of the S-100 framework. The proposed model enables previously unstructured casualty data to be standardized and spatially integrated into digital chart systems. This approach allows accident information to be used alongside other S-100-based data models, contributing to risk-aware route planning and future applications in smart ship operations and maritime safety services.

In the field of geographic information, research on identifying and integrating duplicate data for quality improvement has been actively conducted, contributing to enhancing the reliability of spatial information-based decision-making. The International Hydrographic Organization (IHO) has standardized hydrographic data through a universal hydrographic data model in the hydrographic field. As multiple institutions are independently modeling the data, duplication is occurring in the process of defining ‘features’, which abstract real-world objects as classes. Quality degradation due to these duplicate features can cause confusion in the decision-making process of smart ships, potentially threatening safe navigation. Previous studies in the hydrographic field have identified duplicate features manually, but continuous updates and an increasing number of products have made it difficult to identify all duplicate features. This study proposes a method to identify duplicate features defined in data models using deep learning-based natural language processing models. Through the proposed method, 313 additional duplicate features that were not previously discovered were identified. This efficient approach to identifying duplicate features is anticipated to support interoperability processes, thereby contributing to the stable operation of smart ships and the enhancement of maritime safety.

In this study, we present a sponge-like lipophilic gel crosslinked with a branched crosslinker as an absorbent for VOC removal. The gel was synthesized by crosslinking the monomer 3-(trimethoxysilyl)propyl methacrylate (TMSPMA) with the branched crosslinker poly(2-propyl aspartamide) grafted methacrylate (PPA-g-MA). The grafted crosslinker, PPA-g-MA, was prepared by introducing acrylate groups as crosslinking moieties to the poly(succinimide) precursor for poly(2-propyl aspartamide) (PPA), which serves as a hydrophobic backbone. Lipophilic gels were synthesized with varying TMSPMA monomer concentrations and freeze-dried to form a porous structure. To evaluate VOC absorption, the toluene removal efficiency of the sponge-like lipophilic gel was tested in a continuous gas flow system. As a result, the optimal TMSPMA monomer content for maximizing toluene removal efficiency was determined. This result suggests that while an increase in silicon content generally enhances VOC removal efficiency, the porous structure of sponge-like lipophilic gels plays a more crucial role in absorption capacity. The collapse of the porous structure, caused by excessive silicon content making the material more rubber-like, explains why there exists an optimal monomer content for effective VOC absorption. Overall, these findings provide valuable insights for developing high-performance VOC absorbents.

Fetal alcohol syndrome (FAS) is a lifelong developmental disability that occurs among individuals with prenatal alcohol exposure (PAE). With improved prediction models, FAS can be diagnosed or treated early, if not completely prevented. In this study, we sought to compare different machine learning algorithms and their FAS predictive performance among women who consumed alcohol during pregnancy. We also aimed to identify which variables (eg, timing of exposure to alcohol during pregnancy and type of alcohol consumed) were most influential in generating an accurate model. Data from the collaborative initiative on fetal alcohol spectrum disorders from 2007 to 2017 were used to gather information about 595 women who consumed alcohol during pregnancy at 5 hospital sites around the United States. To obtain information about PAE, questionnaires or in-person interviews, as well as reviews of medical, legal, or social service records were used to gather information about alcohol consumption. Four different machine learning algorithms (logistic regression, XGBoost, light gradient-boosting machine, and CatBoost) were trained to predict the prevalence of FAS at birth, and model performance was measured by analyzing the area under the receiver operating characteristics curve (AUROC). Of the total cases, 80% were randomly selected for training, while 20% remained as test data sets for predicting FAS. Feature importance was also analyzed using Shapley values for the best-performing algorithm. Overall, there were 20 cases of FAS within a total population of 595 individuals with PAE. Most of the drinking occurred in the first trimester only (n=491) or throughout all 3 trimesters (n=95); however, there were also reports of drinking in the first and second trimesters only (n=8), and 1 case of drinking in the third trimester only (n=1). The CatBoost method delivered the best performance in terms of AUROC (0.92) and area under the precision-recall curve (AUPRC 0.51), followed by the logistic regression method (AUROC 0.90; AUPRC 0.59), the light gradient-boosting machine (AUROC 0.89; AUPRC 0.52), and XGBoost (AUROC 0.86; AURPC 0.45). Shapley values in the CatBoost model revealed that 12 variables were considered important in FAS prediction, with drinking throughout all 3 trimesters of pregnancy, maternal age, race, and type of alcoholic beverage consumed (eg, beer, wine, or liquor) scoring highly in overall feature importance. For most predictive measures, the best performance was obtained by the CatBoost algorithm, with an AUROC of 0.92, precision of 0.50, specificity of 0.29, F1 score of 0.29, and accuracy of 0.96. Machine learning algorithms were able to identify FAS risk with a prediction performance higher than that of previous models among pregnant drinkers. For small training sets, which are common with FAS, boosting mechanisms like CatBoost may help alleviate certain problems associated with data imbalances and difficulties in optimization or generalization.

Sources of healthcare big data that provides clinical intuition are not only limited to in-hospital data, but extend to more non-traditional sources such as social media platforms such as X/Twitter, or clinic attendance data.Tumaliuan et al.developed a two-stage depression symptom detection model using multi-lingual data from social media (X/Twitter), demonstrating how digital traces of language and behavior can indicate mental health status. Building explanations or white-box ML techniques are crucial across all medical domains, where trust and verification of automated findings are required before they inform patient care.Yamga et al.applied unsupervised learning to multimodal COVID-19 data to identify patient phenotypes stratified by risk profile, demonstrating the potential to support targeted management early in a patient's hospital admission. Social media analysis can identify mental health risks (Tumaliuan et al.), while phenotype clustering helps tailor treatments (Yamga et al.).

Mild cognitive impairment (MCI) poses significant challenges in early diagnosis and timely intervention. Underdiagnosis, coupled with the economic and social burden of dementia, necessitates more precise detection methods. Machine learning (ML) algorithms show promise in managing complex data for MCI and dementia prediction. This study assessed the predictive accuracy of ML models in identifying the onset of MCI and dementia using the Korean Longitudinal Study of Aging (KLoSA) dataset. This study used data from the KLoSA, a comprehensive biennial survey that tracks the demographic, health, and socioeconomic aspects of middle-aged and older Korean adults from 2018 to 2020. Among the 6171 initial households, 4975 eligible older adult participants aged 60 years or older were selected after excluding individuals based on age and missing data. The identification of MCI and dementia relied on self-reported diagnoses, with sociodemographic and health-related variables serving as key covariates. The dataset was categorized into training and test sets to predict MCI and dementia by using multiple models, including logistic regression, light gradient-boosting machine, XGBoost (extreme gradient boosting), CatBoost, random forest, gradient boosting, AdaBoost, support vector classifier, and k-nearest neighbors, and the training and test sets were used to evaluate predictive performance. The performance was assessed using the area under the receiver operating characteristic curve (AUC). Class imbalances were addressed via weights. Shapley additive explanation values were used to determine the contribution of each feature to the prediction rate. Among the 4975 participants, the best model for predicting MCI onset was random forest, with a median AUC of 0.6729 (IQR 0.3883-0.8152), followed by k-nearest neighbors with a median AUC of 0.5576 (IQR 0.4555-0.6761) and support vector classifier with a median AUC of 0.5067 (IQR 0.3755-0.6389). For dementia onset prediction, the best model was XGBoost, achieving a median AUC of 0.8185 (IQR 0.8085-0.8285), closely followed by light gradient-boosting machine with a median AUC of 0.8069 (IQR 0.7969-0.8169) and AdaBoost with a median AUC of 0.8007 (IQR 0.7907-0.8107). The Shapley values highlighted pain in everyday life, being widowed, living alone, exercising, and living with a partner as the strongest predictors of MCI. For dementia, the most predictive features were other contributing factors, education at the high school level, education at the middle school level, exercising, and monthly social engagement. ML algorithms, especially XGBoost, exhibited the potential for predicting MCI onset using KLoSA data. However, no model has demonstrated robust accuracy in predicting MCI and dementia. Sociodemographic and health-related factors are crucial for initiating cognitive conditions, emphasizing the need for multifaceted predictive models for early identification and intervention. These findings underscore the potential and limitations of ML in predicting cognitive impairment in community-dwelling older adults.

Bipolar disorder (BD) shows heterogeneous illness presentation both cross-sectionally and longitudinally. This phenotypic heterogeneity might reflect underlying genetic heterogeneity. At the same time, overlapping characteristics between BD and other psychiatric illnesses are observed at clinical and biomarker levels, which implies a shared biological mechanism between them. Incorporating these two issues in a single study design, this study investigated whether phenotypically heterogeneous subtypes of BD have a distinct polygenic basis shared with other psychiatric illnesses. Six lifetime phenotype dimensions of BD identified in our previous study were used as target phenotypes. Associations between these phenotype dimensions and polygenic risk scores (PRSs) of major psychiatric illnesses from East Asian (EA) and other available populations were analyzed. Each phenotype dimension showed a different association pattern with PRSs of mental illnesses. PRS for EA schizophrenia showed a significant negative association with the cyclicity dimension ( = 0.044) but a significant positive association with the psychotic/irritable mania dimension ( = 0.001). PRS of EA major depressive disorder demonstrated a significant negative association with the elation dimension ( = 0.003) but a significant positive association with the comorbidity dimension ( = 0.028). This study demonstrates that well-defined phenotype dimensions of lifetime-basis in BD have distinct genetic risks shared with other major mental illnesses. This finding supports genetic heterogeneity in BD and suggests a pleiotropy among BD subtypes and other psychiatric disorders beyond BD. Further genomic analyses adopting deep phenotyping across mental illnesses in ancestrally diverse populations are warranted to clarify intra-diagnosis heterogeneity and inter-diagnoses commonality issues in psychiatry.

The integration of nanoparticles (NPs) into molecular self-assemblies has been extensively studied with the aim of building well-defined, ordered structures which exhibit advanced properties and performances. This study demonstrates a novel strategy for the preparation of a spike-like self-assembly designed to enhance UV blocking. Poly(2-hydroxyethyl aspartamide) (PHEA) substituted with octadecyl chains and menthyl anthranilate (C18-M-PHEA) was successfully synthesized by varying the number of grafted groups to control their morphology and UV absorption. The in situ incorporation of polymerized rod-like TiO2 within the C18-M-PHEA self-aggregates generated spike-like self-assemblies (TiO2@C18-M-PHEA) with a chestnut burr structure in aqueous solution. The results showed that the spike-like self-assemblies integrated with TiO2 NPs exhibited a nine-fold increase in UV protection by simultaneous UV absorption and scattering compared with the pure TiO2 NPs formed via a bulk mixing process. This work provides a novel method for UV protection using self-assembling poly(amino acid)s derivatives integrated with functional nanoparticles to tune their morphology and organization.